Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals. Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease. In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

Despite the inclusion of inherited myeloid malignancies as a separate entity in the World Health Organization Classification, many established predisposing loci continue to lack functional characterization. While germline mutations in the DNA repair factor ERCC excision repair 6 like 2 (ERCC6L2) give rise to bone marrow failure and acute myeloid leukaemia, their consequences on normal haematopoiesis remain unclear. To functionally characterise the dual impact of germline ERCC6L2 loss on human primary haematopoietic stem/progenitor cells (HSPCs) and mesenchymal stromal cells (MSCs), we challenged ERCC6L2-silenced and patient-derived cells ex vivo. Here, we show for the first time that ERCC6L2-deficiency in HSPCs significantly impedes their clonogenic potential and leads to delayed erythroid differentiation. This observation was confirmed by CIBERSORTx RNA-sequencing deconvolution performed on ERCC6L2-silenced erythroid-committed cells, which demonstrated higher proportions of polychromatic erythroblasts and reduced orthochromatic erythroblasts versus controls. In parallel, we demonstrate that the consequences of ERCC6L2-deficiency are not limited to HSPCs, as we observe a striking phenotype in patient-derived and ERCC6L2-silenced MSCs, which exhibit enhanced osteogenesis and suppressed adipogenesis. Altogether, our study introduces a valuable surrogate model to study the impact of inherited myeloid mutations and highlights the importance of accounting for the influence of germline mutations in HSPCs and their microenvironment.

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

Myelodysplastic syndromes (MDS) are hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis, with increased incidence in older individuals. Here we analyze the transcriptome of human HSCs purified from young and older healthy adults, as well as MDS patients, identifying transcriptional alterations following different patterns of expression. While aging-associated lesions seem to predispose HSCs to myeloid transformation, disease-specific alterations may trigger MDS development. Among MDS-specific lesions, we detect the upregulation of the transcription factor DNA Damage Inducible Transcript 3 (DDIT3). Overexpression of DDIT3 in human healthy HSCs induces an MDS-like transcriptional state, and dyserythropoiesis, an effect associated with a failure in the activation of transcriptional programs required for normal erythroid differentiation. Moreover, DDIT3 knockdown in CD34+ cells from MDS patients with anemia is able to restore erythropoiesis. These results identify DDIT3 as a driver of dyserythropoiesis, and a potential therapeutic target to restore the inefficient erythroid differentiation characterizing MDS patients. © 2022, The Author(s).

Multiple myeloma (MM) is an incurable disease, whose clinical heterogeneity makes its management challenging, highlighting the need for biological features to guide improved therapies. Deregulation of specific long non-coding RNAs (lncRNAs) has been shown in MM, nevertheless, the complete lncRNA transcriptome has not yet been elucidated. In this work, we identified 40,511 novel lncRNAs in MM samples. lncRNAs accounted for 82% of the MM transcriptome and were more heterogeneously expressed than coding genes. A total of 10,351 overexpressed and 9,535 downregulated lncRNAs were identified in MM patients when compared with normal bone-marrow plasma cells. Transcriptional dynamics study of lncRNAs in the context of normal B-cell maturation revealed 989 lncRNAs with exclusive expression in MM, among which 89 showed de novo epigenomic activation. Knockdown studies on one of these lncRNAs, SMILO (specific myeloma intergenic long non-coding RNA), resulted in reduced proliferation and induction of apoptosis of MM cells, and activation of the interferon pathway. We also showed that the expression of lncRNAs, together with clinical and genetic risk alterations, stratified MM patients into several progression-free survival and overall survival groups. In summary, our global analysis of the lncRNAs transcriptome reveals the presence of specific lncRNAs associated with the biological and clinical behavior of the disease.

Multiple myeloma (MM) is a plasma cell neoplasm associated with a broad variety of genetic lesions. In spite of this genetic heterogeneity, MMs share a characteristic malignant phenotype whose underlying molecular basis remains poorly characterized. In the present study, we examined plasma cells from MM using a multi-epigenomics approach and demonstrated that, when compared to normal B cells, malignant plasma cells showed an extensive activation of regulatory elements, in part affecting coregulated adjacent genes. Among target genes up-regulated by this process, we found members of the NOTCH, NF-kB, MTOR signaling, and TP53 signaling pathways. Other activated genes included sets involved in osteoblast differentiation and response to oxidative stress, all of which have been shown to be associated with the MM phenotype and clinical behavior. We functionally characterized MM-specific active distant enhancers controlling the expression of thioredoxin (TXN), a major regulator of cellular redox status and, in addition, identified PRDM5 as a novel essential gene for MM. Collectively, our data indicate that aberrant chromatin activation is a unifying feature underlying the malignant plasma cell phenotype.

Long Non-Coding RNAs (lncRNAs) are functional RNAs longer than 200 nucleotides in length. Several lncRNAs are involved in cell proliferation and are deregulated in several human tumors. Few lncRNAs have been described to play a role in Acute Lymphoblastic Leukemia (ALL). In this study, we carried out a genome wide lncRNA expression profiling in ALL samples and peripheral blood samples obtained from healthy donors. We detected 43 lncRNAs that were aberrantly expressed in ALL. Interestingly, among them, linc-PINT showed a significant downregulation in T and B-ALL. Re-expression of linc-PINT in ALL cells induced inhibition of leukemic cell growth that was associated with apoptosis induction and cell cycle arrest in G2/M phase. linc-PINT induced the transcription of HMOX1 which reduced the viability of ALL cells. Intriguingly, we observed that treatment with anti-tumoral epigenetic drugs like LBH-589 (Panobinostat) and Curcumin induced the expression of linc-PINT and HMOX1 in ALL. These results indicate that the downregulation of linc-PINT plays a relevant role in the pathogenesis of ALL, and linc-PINT re-expression may be one of the mechanisms exerted by epigenetic drugs to reduce cell proliferation in ALL.

In recent years, the relevance of RNA metabolism has been increasingly recognized in a variety of diseases. Modifications in the levels of RNA-binding proteins elicit changes in the expression of cancer-related genes. Here we evaluate whether SRSF1 regulates the expression of DNA repair genes, and whether this regulation has a relevant role in lung carcinogenesis. An in silico analysis was performed to evaluate the association between the expression of SRSF1 and DNA repair genes. In vitro functional analyses were conducted in SRSF1 or DNA ligase 1 (LIG1)-downregulated non-small cell lung cancer (NSCLC) cell lines. In addition, the prognostic value of LIG1 was evaluated in NSCLC patients by immunohistochemistry. We found a significant correlation between the DNA repair gene LIG1 and SRSF1 in NSCLC cell lines. Moreover, SRSF1 binds to LIG1 mRNA and regulates its expression by increasing its mRNA stability and enhancing its translation in an mTOR-dependent manner. Furthermore, siRNA-mediated LIG1 inhibition reduced proliferation and increased apoptosis of NSCLC cells. Finally, the expression of LIG1 was an independent prognostic factor for NSCLC, as confirmed in a series of 210 patients. These results show that LIG1 is regulated by the oncoprotein SRSF1 and plays a relevant role in lung cancer cell proliferation and progression. LIG1 is associated with poor prognosis in non-small lung cancer patients.

Purpose: Antiangiogenic therapies targeting the vascular endothelial growth factor (VEGF) pathway have yielded more modest clinical benefit to patients with non-small-cell lung cancer (NSCLC) than initially expected. Clinical data suggest a distinct biologic role of the VEGF pathway in the different histologic subtypes of lung cancer. To clarify the influence of histologic differentiation in the prognostic relevance of VEGF-mediated signaling in NSCLC, we performed a concomitant analysis of the expression of three key elements of the VEGF pathway in the earliest stages of the following two principal histologic subtypes: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). Patients and Methods: We evaluated tumor cell expression of VEGF, VEGF receptor (VEGFR) 1, and VEGFR2 using automatic immunostaining in a series of 298 patients with early-stage NSCLC recruited as part of the multicenter European Early Lung Cancer Detection Group project. A score measuring the VEGF signaling pathway was calculated by adding the tumor cell expression value of VEGF and its two receptors. The results were validated in two additional independent cohorts of patients with NSCLC. Results: The combination of high VEGF, VEGFR1, and VEGFR2 protein expression was associated with lower risk of disease progression in early SCC (univariate analysis, P = .008; multivariate analysis, hazard ratio, 0.62; 95% CI, 0.42 to 0.92; P = .02). The results were validated in two independent patient cohorts, confirming the favorable prognostic value of high VEGF signaling score in early lung SCC. Conclusion: Our results clearly indicate that the combination of high expression of the three key elements in the VEGF pathway is associated with a good prognosis in patients with early SCC but not in patients with ADC.

Background: Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. Results: The array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. Conclusions: This methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.

The study of nutrigenetics and nutrigenomics requires an identification of the genetic background of an organism to determine how this affects nutrient metabolism and the effects that the nutrients have on it. Advances in sequencing technologies have caused a shift from gene-based to genome-wide analyses that have provided information previously unsuspected and unavailable. Although these techniques are commonly used, they are still evolving and susceptible to improvement: therefore, some potential biases need to be considered regarding their performance and use. In this chapter, we describe the most common techniques used to study nutrigenetics and nutrigenomics, focusing on key steps to be followed to obtain reliable results.